import torch.nn as nn
eightbit_only_name = ["down_proj",  "fc_out", "dense_4h_to_h"]

weight_only_map = {
    "GPTJForCausalLM":    ["fc_out"], 
    "LlamaForCausalLM":   [],
    "AquilaForCausalLM":  ["down_proj"],
    "BaichuanForCausalLM": ["down_proj"],
    "MistralForCausalLM": ["down_proj"],
    "FalconForCausalLM" :  [],
    "ChatGLMModel" : [],
    "Qwen2ForCausalLM" : []
}

def get_named_linears(module):
    return {name: m for name, m in module.named_modules() if isinstance(m, nn.Linear)}

def get_op_by_name(module, op_name):
    # get the op by its name relative to the module
    for name, m in module.named_modules():
        if name == op_name:
            return m
    raise ValueError(f"Cannot find op {op_name} in module {module}")


def set_op_by_name(layer, name, new_module):
    levels = name.split('.')
    if len(levels) > 1:
        mod_ = layer
        for l_idx in range(len(levels)-1):
            if levels[l_idx].isdigit():
                mod_ = mod_[int(levels[l_idx])]
            else:
                mod_ = getattr(mod_, levels[l_idx])
        setattr(mod_, levels[-1], new_module)
    else:
        setattr(layer, name, new_module)


def get_op_name(module, op):
    # get the name of the op relative to the module
    for name, m in module.named_modules():
        if m is op:
            return name
    raise ValueError(f"Cannot find op {op} in module {module}")


def append_str_prefix(x, prefix):
    if isinstance(x, str):
        return prefix + x
    elif isinstance(x, tuple):
        return tuple([append_str_prefix(y, prefix) for y in x])
    elif isinstance(x, list):
        return [append_str_prefix(y, prefix) for y in x]
    else:
        return x